What We'll Cover
We will take you through the ins & outs of sports betting analytics system & predictive techniques behind our algorithm.
Units Returned
We pull back the curtain on deceptive marketing tactics & show why units returned, not win percentage, is the deciding factor in making a profit.
Feature Engineering
While we use a variety of metrics in our models, we will explore: individual player-level ability, team-level performance, & team-level play style.
The Model
We tie everything together to show you how our model uses proprietary metrics to create a revolutionary algorithm.
Units Returned
Money Motivated
We designed our system to maximize units returned.
Units returned is the single most important factor in becoming a successful sports bettor. Units returned translates into profit, which generates a return on your investment.
There’s a lot of false marketing in the industry that showcases win percentage as the most important factor in sports betting. While win percentage is important, we must consider the method behind it & understand that not all wins are the same.
This is where we break the stigma. We are fully transparent in everything we do & answer the most important question in sports betting:
“Am I making money?”
SBIA's units returned from the last 5 years in the NBA.
" -" represents covid effected months.
" / " represents non wagered months.
.png)
Let's Apply This Theory
Let's take 10 games using the moneyline as an example. If you were to have a 7-3 record, that is a 70% win percentage. This is considered a phenomenal win percentage, but the odds wagered for these 10 games matter. Let’s say that you have a $400 unit size, & all 10 games you wagered were at -200 odds. With a 7-3 record you would have generated .5 units, which is a profit of $200.
Unit Size:
Total Amount Wagered:
7 Games Won:
3 Games Lost:
Profit:
Units Generated:
$400
$4,000 ($400 x 10)
$1,400 ($400 x 7 / 2)
- $1,200 ($400 x -3)
$200 ($1,400 - $1,200)
.5 unit ($200 / $400)
2017-18
2020-21
2018-19
2021-22
2019-20
SBIA's percentage of games wagered within a single NBA season.
Percentage of games wagered
1071 games
880 games
1001 games
855 games
932 games
Play the Numbers
Our methodology maximizes unit return by utilizing a large volume of plays. A large volume of plays has two primary benefits: stable performance & bankroll turnover.
We treat sports betting similar to how the S&P 500 approaches investments. The larger the sample size, the more stable the market. By wagering on a larger volume of games, we decrease the volatility that is involved in this environment.
This allows us to build a diverse portfolio for our clients, stabilizing long-term performance through a difficult stretch in the market.
Turn Up the Volume
The second benefit for a large volume of plays is bankroll turnover. While we provide a diversification effect similar to the S&P 500, our advantage here is that a traditional investment will take your $10,000 and provide an annual 17% return, leaving you with $11,700. Let us show you how we can do better.
Volume & short-term expirations provide the opportunity to turn over your bankroll effectively, investing far more than the initial capital required to enter the market. At a 2% unit & 4,000 picks a year, you have turned over your bankroll 80 times, investing $800,000. Equivalently, we only have to produce a 0.21% return per dollar invested to generate the same $1,700 profit the S&P 500 did.
The reality is that we can generate 3-5% per dollar, say $32,000, which is a 320% ROI. Volume gives us an opportunity to turn a small initial capital investment into a large yielding investment. This is something that traditional markets cannot provide.
%
320
Bankroll Management
Now that you understand why our methodology utilizes volume, it is vital to understand the importance of bankroll management.
Bankroll management is the key to sustainable sports betting. Our system can deliver a large volume of picks on any given gameday.
In order to place all of the given wagers, it is important to effectively manage your bankroll. A unit size of 2.5% provides the stability to mitigate any short term volatility.
Feature Engineering
Individual
Player -Level Ability
For individual player-level ability, we track a player’s improvement or decline in performance over time by monitoring the outcome of every play in which a player is in a game & updating our valuation of their abilities from each possession.
Let’s take a look at LeBron James's player valuations over two seasons:
In 2020-2021, James played only 45 games due to injury, averaging 25.0 points, 33.4 minutes, & had a 51.3% field goal percentage. While James was limited in his time on the court, our metrics showed he had a relatively high offensive & defensive valuation throughout the season.
Compared to the 2021-2022 season, James played 56 games, averaging 30.3 points, 37.2 minutes, & had a 52.4% field goal percentage. Despite playing more games & averaging better statistics, our metrics showed that he was less effective than the previous year. This is a testament to how individual player-level ability is unique in analyzing data and how we utilize it.
LeBron James (2020 -2021)
Offense
Defense
LeBron James (2021 -2022)
Grizzlies
Nets
Celtics
Team -Level Performance
Monitoring team-level performances is very similar. Game results are the most basic indicator of a team’s ability. However, to pick up on the long-term changes that are not the product of chance by two teams, we consider the randomness of these results.
We can see this in the 2021-2022 NBA ratings for the Grizzlies, Nets, & Celtics.
You can see the result of the Nets consistent off-the-court & injury issues for most of the season, the Grizzlies breakout season behind their First Team All-Star, Ja Morant, & the Celtics going on a historic run in the second half of the season, despite their early struggles.
Team - Level Play Style
Pace is a commonly used metric to determine how fast a team has played in their previous games, which is approximately how many possessions each team gets in 48 minutes.
The major flaw with pace, however, is it tells you how fast a game was played, but not how fast each individual team attempts to play the game. This traditional pace metric can fail when you use it to predict a future matchup.
For this reason we create a team specific pace metric that dials in on each team’s speed of play. This can then be used to better predict the pace of future matchups with different opponents.
The Model
Applying Pressure
Now that we have explored a few of our metrics, we plug this data into the model to project win probabilities, point differentials, & point totals for each team. We compare these projections to the available consensus line against the spread, totals, & moneyline markets to determine an expected value of each option.
Expected value is the measure of how much we expect to win if a game was repeated an infinite number of times. If the expected value is positive, that’s a game we will play.
However, we live in a random world where each event is only observed once and the outcome is binary. An individual game cannot provide the expected value alone. To achieve this, we rely on volume, allowing our edge to prevail.
Money by the Markets
Our model identifies positive expected value bets across several markets: first quarter, first half & full game moneylines, first half & full game spreads, full game totals, & full game team totals. We could provide you picks in all seven, none, or any combination of these markets on any given gameday.
First Half Moneyline
SBIA's overall sides market results from the 2017 -2021 NBA seasons.
First Quarter Moneyline
First Half Spread
Full Game Moneyline
Full Game Spread
All Side Markets
Units Units Units
Depending on the expected value of a game, we allocate 1 unit, 1.5 units, or 2.0 units to each pick. The number of picks we have over correlated markets is also considered in how many units will be allocated.
For example, if we have picks on the full game moneyline & the full game against the spread, we will have a max of 1 unit each since the outcomes of those markets are highly correlated.
SBIA's pick delivery example.
Pick Delivery
Lines get more accurate as game time approaches because, as more money enters the market, bookmakers make adjustments. However, there is a trade-off between access to a softer line & access to more information.
Our research has optimized this trade-off to provide picks at the most opportune times. We deliver our NBA totals & all NCAA picks to our clients from approximately
7:55 - 8:05 AM EST, as we’ve found these are less affected by injury news. As for our NBA spread & moneyline picks, we deliver these at approximately 1:00 PM EST to incorporate additional news.
In the current environment, impact players are being declared out more frequently later in the day compared to prior years & at times multiple starters declared out due to teams' decisions to manage playing time, especially in back-to-back situations. Due to these observations, we will hold case-sensitive game picks until approximately 4:15 PM EST.
Team Totals
Full Game Total
Combined
SBIA's overall back testing results from the 2017-2021 NBA seasons.
Let's Recap
Units Returned
Our methodology focuses on units returned to maximize your revenue opportunities.
Feature Engineering
We showed you how we combine a variety of metrics for model accuracy.
The Model
Our metrics & extensive backtesting produce a model designed for superior performance.
At SBIA, we are dedicated to taking the handicapping profession to new heights. We push the boundaries of what is accepted & what can be accomplished.
Are you ready to elevate your game? Click below to see our pricing packages & select the one that is the best fit for you.
Every one of our picks is displayed no matter the outcome, backed by our
100% Money - Back Guarantee.
Our Promise
Ready to join SBIA?
Check out our packages to see which is the best fit for you.
Elevate Your Game
Still have questions?
Contact us or set up a
one-on-one call with one of our specialists.